Lecture 10 - Regression and Linear Models

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Lecture 9: Review

Covered

Correlation analysis: measuring relationships between variables
The distinction between correlation and regression
Simple linear regression: predicting one variable from another
Estimating and interpreting regression parameters
Testing assumptions and handling violations
Analysis of variance in regression
Model selection and comparison

Lecture 10: Overview

Linear regression:

REGRESSIONS:
- Analysis of variance
- Explained variance
- Assumptions and diagnostics
- Dealing w violations
- Model II regression
- Robust regression
Smoothing Regressions

Lecture 10: Linear Regression

Simple Linear Regression Model

Simple linear regression models the relationship between a response variable (Y) and a predictor variable (X).

The sample regression equation is:

\[\hat{Y} = a + bX\]

Where:

\(\hat{Y}\) is the predicted value of Y
a is the estimate of α (intercept) sometimes \(\beta_0\)
b is the estimate of β (slope) sometimes \(\beta_1\)

Method of Least Squares: The line is chosen to minimize the sum of squared vertical distances (residuals) between observed and predicted Y values.

Lecture 10: Linear Regression

Simple Linear Regression Model

Simple linear regression models the relationship between a response variable (Y) and a predictor variable (X).

The sample regression equation is:

\[\hat{Y} = a + bX\]

Where:

\(\hat{Y}\) is the predicted value of Y
a is the estimate of α (intercept) sometimes \(\beta_0\)
b is the estimate of β (slope) sometimes \(\beta_1\)

Method of Least Squares: The line is chosen to minimize the sum of squared vertical distances (residuals) between observed and predicted Y values.

Lecture 10: Linear Regression

Simple Linear Regression Model

male lions develop more black pigmentation on their noses as they age.
can be used to estimate the age of lions in the field.


Call:
lm(formula = age_years ~ proportion_black, data = lion_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5449 -1.1117 -0.5285  0.9635  4.3421 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.8790     0.5688   1.545    0.133    
proportion_black  10.6471     1.5095   7.053 7.68e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.669 on 30 degrees of freedom
Multiple R-squared:  0.6238,    Adjusted R-squared:  0.6113 
F-statistic: 49.75 on 1 and 30 DF,  p-value: 7.677e-08

Lecture 10: Linear Regression

Simple Linear Regression Model

The calculation for slope (b) is:
\[b = \frac{\sum_i(X_i - \bar{X})(Y_i - \bar{Y})}{\sum_i(X_i - \bar{X})^2}\]

Given: -

\(\bar{X} = 0.3222\)

\(\bar{Y} = 4.3094\)

\(\sum_i(X_i - \bar{X})^2 = 1.2221\)

\(\sum_i(X_i - \bar{X})(Y_i - \bar{Y}) = 13.0123\)

b = 13.0123 / 1.2221 = 10.647

Intercept (a):

\(a = \bar{Y} - b\bar{X} = 4.3094 - 10.647(0.3222) = 0.879\)

Making predictions:

To predict the age of a lion with 0.50 proportion of black on its nose:

\[\hat{Y} = 0.88 + 10.65(0.50) = 6.2 \text{ years}\]

Confidence intervals vs. Prediction intervals:
- Confidence interval: Range for the mean age of all lions with 0.50 black
- Prediction interval: Range for an individual lion with 0.50 black
Both intervals are narrowest near \(\bar{X}\) and widen as X moves away from the mean.

Lecture 10: Linear Regression - estimates of error and significance

In addition to getting estimates of population parameters (intercept - β0 , slope - β1)

want to test hypotheses about them

This is accomplished by analysis of variance
Partition variance in Y: due to variation in X, due to other things (error)

Lecture 9: Linear Regression - estimates of variance

Total variation in Y is “partitioned” into 3 components:

\(SS_{regression}\): variation explained by regression
- difference between predicted values (ŷi ) and mean y (ȳ)
- dfs= 1 for simple linear (parameters-1)
\(SS_{residual}\): variation not explained by regression
- difference between observed (\(y_i\)) and predicted (\(\hat{y}_i\)) values
- dfs= n-2
\(SS_{total}\): total variation
- sum of squared deviations of each observation (\(y_i\)) from mean (\(\bar{y}\))
- dfs = n-1

Lecture 9: Linear Regression - estimates of variance

Total variation in Y is “partitioned” into 3 components:

\(SS_{regression}\): variation explained by regression
- difference between predicted values (ŷi ) and mean y (ȳ)
- dfs= 1 for simple linear (parameters-1)
\(SS_{residual}\): variation not explained by regression
- difference between observed (\(y_i\)) and predicted (\(\hat{y}_i\)) values
- dfs= n-2
\(SS_{total}\): total variation
- sum of squared deviations of each observation (\(y_i\)) from mean (\(\bar{y}\))
- dfs = n-1

Lecture 10: Linear Regression - estimates of variance

Total variation in Y is “partitioned” into 3 components:

\(SS_{regression}\): variation explained by regression
- GREATER IN C than D
\(SS_{residual}\): variation not explained by regression
- GREATER IN B THAN A
\(SS_{total}\): total variation

Lecture 10: Linear Regression - estimates of variance

Sums of Squares and degress of freedome are:

\(SS_{regression} +SS_{residual} = SS_{total}\)

\(df_{regression}+df_{residual} = df_{total}\)

Sums of Squares depends on n
We need a different estimate of variance

Lecture 10: Linear Regression - estimates of variance

Sums of Squares converted to Mean Squares

Sums of Squares divided by degrees of freedom - does not depend on n
\(MS_{residual}\): estimate population variation
\(MS_{regression}\): estimate pop variation and variation due to X-Y relationship
Mean Squares are not additive

Lecture 10: Linear Regression - Null Hypothesis

Regression typically tests null hypothesis that β1 = 0

or no relationship between X and Y

Can test in two ways:

Using t-statistic:

\[t=\frac{b_1-\theta}{s_{b_{1}}}\]

\(s_{b_{1}}\)= Standard error of slope estimate
Bo= 0: t-test: \(t=\frac{b_o}{s_{b_{o}}}\)
1 parameter t-test, where testing whether β1 =0
t-statistic test is more general
R can provide both
Can also ask whether β0 =0 using t-test
or whether two regression lines are significantly different

Lecture 10: Linear Regression - Null Hypothesis

Regression typically tests null hypothesis that β1 = 0

or no relationship between X and Y

Can test in two ways:

Using F-ratio:

\[F = \frac {MS_{regression}}{MS_{residual}}\]

if β1 = 0, ratio will be = 1 otherwise >1
compare F-ratio to df-specific F-distribution
decide how likely obtain our F-ratio by chance

Lecture 10: Linear Regression - Explained variance

Want to know how strong is association between X and Y
Coefficient of determination (\(R^2\)): proportion of variation in Y explained by X

\[r^2 = \frac{SS_{regression}}{SS_{total}}=1-\frac{SS_{residual}}{SS_{total}}\]

When more of variation is due to regression rather than ‘error’, \(R^2\) closer to 1

Lecture 10: Linear Regression - Explained variance

Want to know how strong is association between X and Y
Coefficient of determination (\(R^2\)): proportion of variation in Y explained by X

\[r^2 = \frac{SS_{regression}}{SS_{total}}=1-\frac{SS_{residual}}{SS_{total}}\]

When more of variation is due to regression rather than ‘error’, \(R^2\) closer to 1

Lecture 10: Linear Regression - Explained variance

\[F = \frac{MS_{regression}}{MS_{residual}}\] \[r^2 = \frac{SS_{regression}}{SS_{total}}\]

summary(lion_model)


Call:
lm(formula = age_years ~ proportion_black, data = lion_data)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.5449 -1.1117 -0.5285  0.9635  4.3421 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)    
(Intercept)        0.8790     0.5688   1.545    0.133    
proportion_black  10.6471     1.5095   7.053 7.68e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 1.669 on 30 degrees of freedom
Multiple R-squared:  0.6238,    Adjusted R-squared:  0.6113 
F-statistic: 49.75 on 1 and 30 DF,  p-value: 7.677e-08

anova(lion_model)

Analysis of Variance Table

Response: age_years
                 Df  Sum Sq Mean Sq F value    Pr(>F)    
proportion_black  1 138.544 138.544   49.75 7.677e-08 ***
Residuals        30  83.543   2.785                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Reporting results

“Lion age (years) could be predicted from nose spots (percentage) using the simple linear regression model age = 10.67 * proportion_black + 0.8749. Regression analysis showed that the slope of the relationship was significantly (at α=0.05) different than 0 (\(F_{1,30}\) = 49.93, p < 0.0001, R² = 0.6247).”

Note there is an adjusted R² - what is that - accounts for the number of predictors in your model - adjusts the R² value by penalizing the addition of variables that don’t improve the model fit significantly

The formula for adjusted R² is:

\[ R^2 = 1 - \frac{(1 - R²) × (n - 1)}{(n - p - 1)}\]

Where:

n is the number of observations (32 lions)
p is the number of predictors (1 = proportion_black)

\(R^2\) measures the proportion of variance in the dependent variable (age_years) that is explained by the independent variable (proportion_black).

Assumptions and diagnostics of regression

Assumptions apply to observed values of Y and εi
most can be assessed by looking at residuals (distance from predicted)

Linearity:

relationship between X and Y in population is straight line

Check:

examine biplot of Y on X

If violated:

transform Y
use polynomial or nonlinear regression

Assumptions and diagnostics of regression

Normality:

y-values for each xi are normally distributed.
OLS estimates moderately robust to violation

Check:

are residuals normally distributed?
qq plots, histogram of residuals, shapiro-wilk test

If violated:

transform Y
use Generalized Linear Model

Assumptions and diagnostics of regression

Homogeneity of variance:

y-values for each xi have same variance.
OLS estimates NOT robust to violation

Check:

plot residuals against x-values or predicted values (ŷi)

If violated:

transform Y
use GLM
weighted LS regression

Assumptions and diagnostics of regression

Independence:

Y values from each xi do not influence each other
Often violated with repeated measurements in time/space -> autocorrelation

Check: determine correlation coefficient bw adjacent residuals

If violate:

ANOVA (grouping present)
mixed model ANOVA
time series

Assumptions and diagnostics of regression

Fixed X:

xi are known values fixed by researcher (e.g., drug doses).
Often not true in ecology.

If violated:

not problem for H-testing, prediction, but
error underestimated.
Can use model II regression

Assumptions and diagnostics of regression

Outlier or Influence:

how much each point affects slope (Cook’s D)
Large Di (>1) indicates influential observation

Assumptions and diagnostics of regression

Residual plots

residuals vs. predicted y:

can be used to assess assumptions:
- linearity
- normality
- equal variance
- outliers

Dealing with violations

Weighted least squares:

when variance unequal, can use WLS approach
each point weighted by reciprocal of variance (points w large variance given less weight)

Robust regression:

when distribution distinctly non-normal and/or large outliers

LAD:

parameters estimated from non-squared residuals
outliers not as influential

M-estimators:

residuals have different weight depending on distance from mean

Rank-based: “if all else fails”

Model II regression

Fixed X is assumption of regular regression

what if X random (typical case)?

If goal is prediction (interpolation) then Model I is ok…
if goal is correct parameters and error estimates, may need to use Model II

Model II regression

Model II regression - Approach underused in ecology

Model I regression will still perform well for H-tests
underestimate true slope
minimizes distance between points and line along both axes
(vs. on Y only in OLS)

MA and RMA approach slightly different

Model II Regression

Detailed Explanation of Model II Regression Types

Standardized Major Axis (SMA)

SMA regression minimizes the product of the vertical and horizontal distances from the points to the regression line. It’s implemented in the smatr package with method=“SMA”. SMA is appropriate when the measurement scales of X and Y are different.

Major Axis (MA)

MA regression minimizes the perpendicular distances from the data points to the regression line. It’s implemented in the smatr package with method=“MA”. MA is appropriate when X and Y are measured in the same units.

Reduced Major Axis (RMA)

RMA regression (also called geometric mean regression) is available in the lmodel2 package. It produces a slope that is the geometric mean of the OLS regression slopes of Y on X and X on Y (specifically, it equals the OLS slope of Y on X multiplied by the sign of the correlation between X and Y, divided by the square root of the R² value).

Model II Regression

When to Use Each Method

OLS (Model I) - Use when:

X is measured without error
The research goal is predicting Y from X
There’s a clear dependent variable

MA (Major Axis) - Use when:

X and Y are measured in the same units
Both variables have similar error variances
The goal is to understand the symmetric relationship

SMA (Standardized Major Axis) - Use when:

X and Y are measured in different units
The goal is to understand the structural relationship
You want to test for isometry or allometry in scaling studies

RMA (Reduced Major Axis) - Use when:

The ratio of error variances is approximately equal to the ratio of the true variances
Both variables contain measurement error
Neither variable is clearly dependent or independent

Model II Regression

Key Differences in Results

The slopes of these methods will typically follow this pattern when the correlation coefficient is less than 1: OLS slope < MA slope < RMA slope < inverse of OLS (X on Y) slope This is particularly evident when the correlation between X and Y is weaker. As correlation approaches 1, the differences between methods diminish.

Model II Regression

Decision Tree

Here’s a simplified decision tree:

-Are X and Y measured with error? If No → Use OLS (Model I) -Are the errors in X and Y approximately equal? If Yes → Use MA -Are X and Y measured in different units/scales? If Yes → Consider SMA -Is the correlation between X and Y weak (<0.7)? If Yes → Method choice is critical; consider RMA -Are you uncertain about error structure? If Yes → RMA is a reasonable compromise

Remember that when the correlation between X and Y is very strong (r > 0.9), all methods will yield similar results, making the choice less critical. The differences between methods become more pronounced as the correlation weakens.

Finally, it’s often valuable to run multiple methods and compare the results. If they lead to different ecological or biological interpretations, this should be explicitly addressed in your discussion.

Model II Regression


Call:
lm(formula = y ~ x, data = data_ols_m2)

Residuals:
   Min     1Q Median     3Q    Max 
-8.058 -3.498 -0.990  2.946 16.070 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.1346     2.6584   1.179    0.241    
x             2.8745     0.2617  10.982   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 4.858 on 98 degrees of freedom
Multiple R-squared:  0.5517,    Adjusted R-squared:  0.5471 
F-statistic: 120.6 on 1 and 98 DF,  p-value: < 2.2e-16


Model II regression

Call: lmodel2(formula = y ~ x, data = data_ols_m2, range.y =
"relative", range.x = "relative", nperm = 99)

n = 100   r = 0.7427668   r-square = 0.5517025 
Parametric P-values:   2-tailed = 9.070988e-19    1-tailed = 4.535494e-19 
Angle between the two OLS regression lines = 8.317517 degrees

Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
A permutation test of r is equivalent to a permutation test of the OLS slope
P-perm for SMA = NA because the SMA slope cannot be tested

Regression results
  Method  Intercept    Slope Angle (degrees) P-perm (1-tailed)
1    OLS   3.134600 2.874473        70.81773              0.01
2     MA -18.688419 5.059928        78.82062              0.01
3    SMA  -6.805843 3.869953        75.51163                NA
4    RMA  -8.131038 4.002664        75.97273              0.01

Confidence intervals
  Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
1    OLS      -2.140952        8.410152   2.355051    3.393894
2     MA     -29.748586      -10.906922   4.280654    6.167542
3    SMA     -12.339088       -1.965648   3.385234    4.424077
4    RMA     -16.211541       -1.554125   3.344023    4.811882

Eigenvalues: 54.08817 1.502864 

H statistic used for computing C.I. of MA: 0.001181286

                2.5 %   97.5 %
(Intercept) -2.140952 8.410152
x            2.355051 3.393894

Call: sma(formula = y ~ x, data = data_ols_m2, method = "SMA") 

Fit using Standardized Major Axis 

------------------------------------------------------------
Coefficients:
             elevation    slope
estimate     -6.805843 3.869953
lower limit -12.094381 3.385234
upper limit  -1.517306 4.424077

H0 : variables uncorrelated
R-squared : 0.5517025 
P-value : < 2.22e-16

Call: sma(formula = y ~ x, data = data_ols_m2, method = "MA") 

Fit using Major Axis 

------------------------------------------------------------
Coefficients:
             elevation    slope
estimate    -18.688419 5.059928
lower limit -27.905282 4.280260
upper limit  -9.471556 6.168337

H0 : variables uncorrelated
R-squared : 0.5517025 
P-value : < 2.22e-16

Other Formats

Lecture 9: Review

Lecture 10: Overview

Lecture 10: Linear Regression

Simple Linear Regression Model

Lecture 10: Linear Regression

Simple Linear Regression Model

Lecture 10: Linear Regression

Simple Linear Regression Model

Lecture 10: Linear Regression

Simple Linear Regression Model

Lecture 10: Linear Regression - estimates of error and significance

Lecture 9: Linear Regression - estimates of variance

Lecture 9: Linear Regression - estimates of variance

Lecture 10: Linear Regression - estimates of variance

Lecture 10: Linear Regression - estimates of variance

Lecture 10: Linear Regression - estimates of variance

Lecture 10: Linear Regression - Null Hypothesis

Using t-statistic:

Lecture 10: Linear Regression - Null Hypothesis

Using F-ratio:

Lecture 10: Linear Regression - Explained variance

Lecture 10: Linear Regression - Explained variance

Lecture 10: Linear Regression - Explained variance

Reporting results

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Assumptions and diagnostics of regression

Dealing with violations

Model II regression

Model II regression

Model II Regression

Detailed Explanation of Model II Regression Types

Model II Regression

When to Use Each Method

Model II Regression

Key Differences in Results

Model II Regression

Decision Tree

Model II Regression